
import os
import numpy as np
import pandas as pd
import requests
import json
import re
import seaborn as sns
from IPython.display import IFrame
from wordcloud import WordCloud
from wordcloud import ImageColorGenerator
from wordcloud import STOPWORDS
from PIL import Image
from io import BytesIO
import matplotlib.pyplot as plt
%matplotlib inline


# Downloading the image-prediction files programmatically. 
url = "https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv"
response = requests.get(url)


folder_name = 'image_predict'
# Make directory if it doesn't already exist
if not os.path.exists(folder_name):
    os.makedirs(folder_name)
    
# writing image-prediction contents to image_predict folder
with open(os.path.join(folder_name, url.split("/")[-1]), mode="wb") as file:
    file.write(response.content)


# twitter archive dataset
twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')
# nueral network dataset
image_predict = pd.read_csv('image_predict/image-predictions.tsv', sep='\t')


# Reading the json file

tweets_list = []

with open('tweets/tweet_json.txt', encoding='UTF-8') as json_file:

    for tweets in json_file:
        tweet = json.loads(tweets)
        # Extract variables from the 'tweet' dictionary
        tweet_id = tweet['id']
        retweet_count = tweet['retweet_count']
        fav_count = tweet['favorite_count']
    
        # Create a dictionary with the JSON data and add it to the 'tweets_list.'
        tweets_list.append({'tweet_id':tweet_id,
                       'retweet_count': retweet_count,
                       'favorite_count': fav_count})

# Convert the data in the 'tweets_data' list to a DataFrame
df_tweet = pd.DataFrame(tweets_list, 
                                   columns = ['tweet_id',
                                              'retweet_count',
                                              'favorite_count'])


twitter_archive


image_predict


df_tweet


twitter_archive.describe()


twitter_archive.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   tweet_id                    2356 non-null   int64  
 1   in_reply_to_status_id       78 non-null     float64
 2   in_reply_to_user_id         78 non-null     float64
 3   timestamp                   2356 non-null   object 
 4   source                      2356 non-null   object 
 5   text                        2356 non-null   object 
 6   retweeted_status_id         181 non-null    float64
 7   retweeted_status_user_id    181 non-null    float64
 8   retweeted_status_timestamp  181 non-null    object 
 9   expanded_urls               2297 non-null   object 
 10  rating_numerator            2356 non-null   int64  
 11  rating_denominator          2356 non-null   int64  
 12  name                        2356 non-null   object 
 13  doggo                       2356 non-null   object 
 14  floofer                     2356 non-null   object 
 15  pupper                      2356 non-null   object 
 16  puppo                       2356 non-null   object 
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB


twitter_archive.doggo.value_counts()

None     2259
doggo      97
Name: doggo, dtype: int64


twitter_archive.query('rating_numerator > 100')


twitter_archive.sample(5)


twitter_archive.tweet_id.duplicated().sum()

0


twitter_archive[twitter_archive.text.str.contains('RT') & twitter_archive.text.str.contains('@')]


image_predict.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   tweet_id  2075 non-null   int64  
 1   jpg_url   2075 non-null   object 
 2   img_num   2075 non-null   int64  
 3   p1        2075 non-null   object 
 4   p1_conf   2075 non-null   float64
 5   p1_dog    2075 non-null   bool   
 6   p2        2075 non-null   object 
 7   p2_conf   2075 non-null   float64
 8   p2_dog    2075 non-null   bool   
 9   p3        2075 non-null   object 
 10  p3_conf   2075 non-null   float64
 11  p3_dog    2075 non-null   bool   
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB


image_predict.sample(5)


image_predict.tweet_id.duplicated().sum()

0


image_predict.describe()


image_predict.jpg_url.duplicated().sum()

66


df_tweet.tail()


df_tweet.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype
---  ------          --------------  -----
 0   tweet_id        2354 non-null   int64
 1   retweet_count   2354 non-null   int64
 2   favorite_count  2354 non-null   int64
dtypes: int64(3)
memory usage: 55.3 KB


df_tweet.tweet_id.duplicated().sum()

0


# Making copies of dataframes
df_image = image_predict.copy()
df_archive = twitter_archive.copy()
df_tweets = df_tweet.copy()


# Extracting the retweets
retweets = df_archive.retweeted_status_id.notnull()


# Dropping the retweets
df_archive = df_archive[~retweets]


# Extracting the replies
replies = df_archive.in_reply_to_status_id.notnull()

# Dropping replies
df_archive = df_archive[~replies]


# dropping redundant columns
df_archive.drop(['in_reply_to_status_id',  'in_reply_to_user_id', 'retweeted_status_id', 'retweeted_status_user_id', 
                 'retweeted_status_timestamp', 'expanded_urls', 'source','name'], axis=1, inplace=True)


# converting timestamp to a datetime data type
df_archive['timestamp'] = pd.to_datetime(df_archive.timestamp)


# Creating a single column for rating
df_archive['rating'] = df_archive.rating_numerator / df_archive.rating_denominator


df_archive.drop(['rating_numerator','rating_denominator'], axis=1, inplace=True)


# Unpivoring the dog names columns
# use a for loop to replace all the 'None'
stage = ['doggo','pupper', 'floofer', 'puppo' ]
for dog in stage:
        df_archive[dog] = df_archive[dog].replace("None", '')


# Creating a new column for dog stage
df_archive['dog_stage'] = df_archive[['doggo', 'floofer', 'pupper','puppo']].agg(''.join, axis=1)

# drop the four old colomns
df_archive = df_archive.drop(['doggo','floofer','pupper','puppo'], axis = 1)

# use np.nan to fill the empty dog stage cells
df_archive['dog_stage'] = df_archive['dog_stage'].replace('', np.nan)


df_archive.dtypes

tweet_id                   int64
timestamp    datetime64[ns, UTC]
text                      object
rating                   float64
dog_stage                 object
dtype: object


df_archive.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2097 entries, 0 to 2355
Data columns (total 5 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   tweet_id   2097 non-null   int64              
 1   timestamp  2097 non-null   datetime64[ns, UTC]
 2   text       2097 non-null   object             
 3   rating     2097 non-null   float64            
 4   dog_stage  336 non-null    object             
dtypes: datetime64[ns, UTC](1), float64(1), int64(1), object(2)
memory usage: 98.3+ KB


df_archive.sample(5)


df_archive[df_archive.text.str.contains('RT') & df_archive.text.str.contains('@')]


df_archive.dog_stage.value_counts()

pupper          221
doggo            72
puppo            23
floofer           9
doggopupper       9
doggopuppo        1
doggofloofer      1
Name: dog_stage, dtype: int64


# drop duplicated image jpg urls
df_image = df_image.drop_duplicates(subset='jpg_url', keep='first')


# the prediction of dog columns into one column
condition_list = [(df_image['p1_dog']==True), 
                 (df_image['p2_dog']==True), 
                 (df_image['p3_dog']==True)]
selection_list = [(df_image['p1']), 
                 (df_image['p2']), 
                 (df_image['p3']) 
                 ]
df_image['dog_type'] = np.select(condition_list, selection_list, default = np.nan)


# converting the three confidence colmns into one
condition_list = [(df_image['p1_dog']==True), 
                 (df_image['p2_dog']==True), 
                 (df_image['p3_dog']==True)]
selection_list = [(df_image['p1_conf']), 
                 (df_image['p2_conf']), 
                 (df_image['p3_conf']) 
                 ]
df_image['confidence'] = np.select(condition_list, selection_list, default = np.nan)


df_image['dog_type'] = df_image.dog_type.str.lower()


df_image = df_image.drop(['p1','p1_dog','p1_conf','p2','p2_dog','p2_conf','p3','p3_dog','p3_conf'], axis=1)


df_clean = pd.merge(df_tweet,df_image, left_on='tweet_id', right_on='tweet_id', how='inner')


# [since text and time from twitter archived will be used for my analysis,]
# perform an inner join to get rid of rows of tweets without [text and timestamp]
df_final = pd.merge(df_clean,df_archive, left_on='tweet_id', right_on='tweet_id', how='inner')


# Reordering the columns in the final dataset before storing
df_final_clean = df_final[['tweet_id','timestamp','text','jpg_url','img_num','dog_type','dog_stage','rating',
                     'confidence','retweet_count','favorite_count']]


df_image.jpg_url.duplicated().sum()

0


df_image.dog_type.value_counts()

golden_retriever        158
labrador_retriever      108
pembroke                 95
chihuahua                91
pug                      63
                       ... 
scotch_terrier            1
entlebucher               1
japanese_spaniel          1
standard_schnauzer        1
bouvier_des_flandres      1
Name: dog_type, Length: 113, dtype: int64


df_clean.sample(5)


df_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2008 entries, 0 to 2007
Data columns (total 7 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   tweet_id        2008 non-null   int64  
 1   retweet_count   2008 non-null   int64  
 2   favorite_count  2008 non-null   int64  
 3   jpg_url         2008 non-null   object 
 4   img_num         2008 non-null   int64  
 5   dog_type        1691 non-null   object 
 6   confidence      1691 non-null   float64
dtypes: float64(1), int64(4), object(2)
memory usage: 125.5+ KB


df_tweets.sample(5)


df_tweets.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype
---  ------          --------------  -----
 0   tweet_id        2354 non-null   int64
 1   retweet_count   2354 non-null   int64
 2   favorite_count  2354 non-null   int64
dtypes: int64(3)
memory usage: 55.3 KB


df_final_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1971 entries, 0 to 1970
Data columns (total 11 columns):
 #   Column          Non-Null Count  Dtype              
---  ------          --------------  -----              
 0   tweet_id        1971 non-null   int64              
 1   timestamp       1971 non-null   datetime64[ns, UTC]
 2   text            1971 non-null   object             
 3   jpg_url         1971 non-null   object             
 4   img_num         1971 non-null   int64              
 5   dog_type        1666 non-null   object             
 6   dog_stage       303 non-null    object             
 7   rating          1971 non-null   float64            
 8   confidence      1666 non-null   float64            
 9   retweet_count   1971 non-null   int64              
 10  favorite_count  1971 non-null   int64              
dtypes: datetime64[ns, UTC](1), float64(2), int64(4), object(4)
memory usage: 184.8+ KB


df_final_clean.tail()


df_final_clean.to_csv('twitter_archive_master.csv', index=False)


fig, ax = plt.subplots(figsize=(15,10))
sns.heatmap(df_final_clean[['rating','retweet_count','favorite_count']].corr(), ax=ax, annot=True);


df_final_clean.dog_type.value_counts()[:3]

golden_retriever      156
labrador_retriever    106
pembroke               94
Name: dog_type, dtype: int64


# Getting an image of the most liked golden_retriever picture
golden_retriever = df_final_clean.query('dog_type == "golden_retriever"').sort_values(by = 'favorite_count',ascending = False)
#Getting the url of golden retriever's most liked picture
url = golden_retriever.jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


# Getting an image of the most liked labrador_retriever picture
golden_retriever = df_final_clean.query('dog_type == "labrador_retriever"').sort_values(by = 'favorite_count',ascending = False)
#Getting the url of golden retriever's most liked picture
url = golden_retriever.jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


# Getting an image of the most liked pembroke picture
golden_retriever = df_final_clean.query('dog_type == "pembroke"').sort_values(by = 'favorite_count',ascending = False)
#Getting the url of golden retriever's most liked picture
url = golden_retriever.jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


#creatinh a function to clean hyperlinks from text column in df_final_clean
def cleanTxt(text):
     text = re.sub(r'https?:\/\/\S+', '', text)
        
     return text

# Cleaning the column
df_final_clean['text'] = df_final_clean.text.apply(cleanTxt)


text = ' '.join( [twts for twts in df_final_clean.text]) #To join all tweet
    # generate word cloud 
wordcloud = WordCloud(width=1000, height = 600,
                      
                      max_words = 100,
                      stopwords = stopwords,
                      background_color="black").generate(text)

  # Display the generated image:
plt.figure( figsize=(20,10), facecolor='k' )
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off");


df_final_clean.rating.max()

177.6


df_final_clean.query('rating == 177.60')


#Getting the url of the dog with the highly rated dog
url = df_final_clean.query('rating == 177.60').jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


df_final_clean.favorite_count.max()

132810


df_final_clean.query('favorite_count == 132810')


#Getting the url of the dog with the highest favorite count
url = df_final_clean.query('favorite_count == 132810').jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


df_final_clean.retweet_count.max()

79515


df_final_clean.query('retweet_count == 79515')


#Getting the url of the dog with the highest favorite count
url = df_final_clean.query('retweet_count == 79515').jpg_url.iloc[0]
response = requests.get(url)
Image.open(BytesIO(response.content))


Dashboard = IFrame(src="https://app.powerbi.com/view?r=eyJrIjoiM2NmMGI2YjItOWU1YS00OWZjLWIxYTAtNGUyNWVjYzNmNDZmIiwidCI6ImE5NjMwYTViLTA5M2EtNDM5Yy04NjM5LThhYmJmMzRhN2M5NyJ9",  width = 1000, height = 600)
display(Dashboard)

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2351	666049248165822465	NaN	NaN	2015-11-16 00:24:50 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a 1949 1st generation vulpix. Enj...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666049248...	5	10	None	None	None	None	None
2352	666044226329800704	NaN	NaN	2015-11-16 00:04:52 +0000	<a href="http://twitter.com/download/iphone" r...	This is a purebred Piers Morgan. Loves to Netf...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666044226...	6	10	a	None	None	None	None
2353	666033412701032449	NaN	NaN	2015-11-15 23:21:54 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a very happy pup. Big fan of well-main...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666033412...	9	10	a	None	None	None	None
2354	666029285002620928	NaN	NaN	2015-11-15 23:05:30 +0000	<a href="http://twitter.com/download/iphone" r...	This is a western brown Mitsubishi terrier. Up...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666029285...	7	10	a	None	None	None	None
2355	666020888022790149	NaN	NaN	2015-11-15 22:32:08 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a Japanese Irish Setter. Lost eye...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666020888...	8	10	None	None	None	None	None

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True
...	...	...	...	...	...	...	...	...	...	...	...	...
2070	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True
2071	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False
2072	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True
2073	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True
2074	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False

	tweet_id	retweet_count	favorite_count
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048
...	...	...	...
2349	666049248165822465	41	111
2350	666044226329800704	147	311
2351	666033412701032449	47	128
2352	666029285002620928	48	132
2353	666020888022790149	532	2535

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	retweeted_status_id	retweeted_status_user_id	rating_numerator	rating_denominator
count	2.356000e+03	7.800000e+01	7.800000e+01	1.810000e+02	1.810000e+02	2356.000000	2356.000000
mean	7.427716e+17	7.455079e+17	2.014171e+16	7.720400e+17	1.241698e+16	13.126486	10.455433
std	6.856705e+16	7.582492e+16	1.252797e+17	6.236928e+16	9.599254e+16	45.876648	6.745237
min	6.660209e+17	6.658147e+17	1.185634e+07	6.661041e+17	7.832140e+05	0.000000	0.000000
25%	6.783989e+17	6.757419e+17	3.086374e+08	7.186315e+17	4.196984e+09	10.000000	10.000000
50%	7.196279e+17	7.038708e+17	4.196984e+09	7.804657e+17	4.196984e+09	11.000000	10.000000
75%	7.993373e+17	8.257804e+17	4.196984e+09	8.203146e+17	4.196984e+09	12.000000	10.000000
max	8.924206e+17	8.862664e+17	8.405479e+17	8.874740e+17	7.874618e+17	1776.000000	170.000000

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
188	855862651834028034	8.558616e+17	1.943518e+08	2017-04-22 19:15:32 +0000	<a href="http://twitter.com/download/iphone" r...	@dhmontgomery We also gave snoop dogg a 420/10...	NaN	NaN	NaN	NaN	420	10	None	None	None	None	None
189	855860136149123072	8.558585e+17	1.361572e+07	2017-04-22 19:05:32 +0000	<a href="http://twitter.com/download/iphone" r...	@s8n You tried very hard to portray this good ...	NaN	NaN	NaN	NaN	666	10	None	None	None	None	None
290	838150277551247360	8.381455e+17	2.195506e+07	2017-03-04 22:12:52 +0000	<a href="http://twitter.com/download/iphone" r...	@markhoppus 182/10	NaN	NaN	NaN	NaN	182	10	None	None	None	None	None
313	835246439529840640	8.352460e+17	2.625958e+07	2017-02-24 21:54:03 +0000	<a href="http://twitter.com/download/iphone" r...	@jonnysun @Lin_Manuel ok jomny I know you're e...	NaN	NaN	NaN	NaN	960	0	None	None	None	None	None
902	758467244762497024	NaN	NaN	2016-07-28 01:00:57 +0000	<a href="http://twitter.com/download/iphone" r...	Why does this never happen at my front door......	NaN	NaN	NaN	https://twitter.com/dog_rates/status/758467244...	165	150	None	None	None	None	None
979	749981277374128128	NaN	NaN	2016-07-04 15:00:45 +0000	<a href="https://about.twitter.com/products/tw...	This is Atticus. He's quite simply America af....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/749981277...	1776	10	Atticus	None	None	None	None
1120	731156023742988288	NaN	NaN	2016-05-13 16:15:54 +0000	<a href="http://twitter.com/download/iphone" r...	Say hello to this unbelievably well behaved sq...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/731156023...	204	170	this	None	None	None	None
1634	684225744407494656	6.842229e+17	4.196984e+09	2016-01-05 04:11:44 +0000	<a href="http://twitter.com/download/iphone" r...	Two sneaky puppers were not initially seen, mo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684225744...	143	130	None	None	None	None	None
1635	684222868335505415	NaN	NaN	2016-01-05 04:00:18 +0000	<a href="http://twitter.com/download/iphone" r...	Someone help the girl is being mugged. Several...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684222868...	121	110	None	None	None	None	None
1779	677716515794329600	NaN	NaN	2015-12-18 05:06:23 +0000	<a href="http://twitter.com/download/iphone" r...	IT'S PUPPERGEDDON. Total of 144/120 ...I think...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/677716515...	144	120	None	None	None	None	None
2074	670842764863651840	NaN	NaN	2015-11-29 05:52:33 +0000	<a href="http://twitter.com/download/iphone" r...	After so many requests... here you go.\n\nGood...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/670842764...	420	10	None	None	None	None	None

PROJECT PREAMBLE¶

Define¶

Code¶

Test¶

Define¶

Code¶

Test¶

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
939	753039830821511168	NaN	NaN	2016-07-13 01:34:21 +0000	<a href="http://vine.co" rel="nofollow">Vine -...	So this just changed my life. 13/10 please enj...	NaN	NaN	NaN	https://vine.co/v/5W2Dg3XPX7a	13	10	None	None	None	None	None
184	856526610513747968	8.558181e+17	4.196984e+09	2017-04-24 15:13:52 +0000	<a href="http://twitter.com/download/iphone" r...	THIS IS CHARLIE, MARK. HE DID JUST WANT TO SAY...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/856526610...	14	10	None	None	None	None	None
1617	685169283572338688	NaN	NaN	2016-01-07 18:41:01 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Gerbald. He just found out he's adopted. ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/685169283...	11	10	Gerbald	None	None	pupper	None
633	793845145112371200	NaN	NaN	2016-11-02 16:00:06 +0000	<a href="http://twitter.com/download/iphone" r...	This is Clark. He was just caught wearing pant...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/793845145...	13	10	Clark	None	None	None	None
535	807059379405148160	NaN	NaN	2016-12-09 03:08:45 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Cali. She arrived preas...	7.829691e+17	4.196984e+09	2016-10-03 15:42:44 +0000	https://twitter.com/dog_rates/status/782969140...	12	10	Cali	None	None	None	None

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
19	888202515573088257	NaN	NaN	2017-07-21 01:02:36 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Canela. She attempted s...	8.874740e+17	4.196984e+09	2017-07-19 00:47:34 +0000	https://twitter.com/dog_rates/status/887473957...	13	10	Canela	None	None	None	None
32	886054160059072513	NaN	NaN	2017-07-15 02:45:48 +0000	<a href="http://twitter.com/download/iphone" r...	RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...	8.860537e+17	1.960740e+07	2017-07-15 02:44:07 +0000	https://twitter.com/dog_rates/status/886053434...	12	10	None	None	None	None	None
36	885311592912609280	NaN	NaN	2017-07-13 01:35:06 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Lilly. She just paralle...	8.305833e+17	4.196984e+09	2017-02-12 01:04:29 +0000	https://twitter.com/dog_rates/status/830583320...	13	10	Lilly	None	None	None	None
68	879130579576475649	NaN	NaN	2017-06-26 00:13:58 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Emmy. She was adopted t...	8.780576e+17	4.196984e+09	2017-06-23 01:10:23 +0000	https://twitter.com/dog_rates/status/878057613...	14	10	Emmy	None	None	None	None
73	878404777348136964	NaN	NaN	2017-06-24 00:09:53 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: Meet Shadow. In an attempt to r...	8.782815e+17	4.196984e+09	2017-06-23 16:00:04 +0000	https://www.gofundme.com/3yd6y1c,https://twitt...	13	10	Shadow	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1023	746521445350707200	NaN	NaN	2016-06-25 01:52:36 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Shaggy. He knows exactl...	6.678667e+17	4.196984e+09	2015-11-21 00:46:50 +0000	https://twitter.com/dog_rates/status/667866724...	10	10	Shaggy	None	None	None	None
1043	743835915802583040	NaN	NaN	2016-06-17 16:01:16 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: Extremely intelligent dog here....	6.671383e+17	4.196984e+09	2015-11-19 00:32:12 +0000	https://twitter.com/dog_rates/status/667138269...	10	10	None	None	None	None	None
1242	711998809858043904	NaN	NaN	2016-03-21 19:31:59 +0000	<a href="http://twitter.com/download/iphone" r...	RT @twitter: @dog_rates Awesome Tweet! 12/10. ...	7.119983e+17	7.832140e+05	2016-03-21 19:29:52 +0000	https://twitter.com/twitter/status/71199827977...	12	10	None	None	None	None	None
2259	667550904950915073	NaN	NaN	2015-11-20 03:51:52 +0000	<a href="http://twitter.com" rel="nofollow">Tw...	RT @dogratingrating: Exceptional talent. Origi...	6.675487e+17	4.296832e+09	2015-11-20 03:43:06 +0000	https://twitter.com/dogratingrating/status/667...	12	10	None	None	None	None	None
2260	667550882905632768	NaN	NaN	2015-11-20 03:51:47 +0000	<a href="http://twitter.com" rel="nofollow">Tw...	RT @dogratingrating: Unoriginal idea. Blatant ...	6.675484e+17	4.296832e+09	2015-11-20 03:41:59 +0000	https://twitter.com/dogratingrating/status/667...	5	10	None	None	None	None	None

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
1841	838083903487373313	https://pbs.twimg.com/media/C6F42cGUYAAIKsX.jpg	2	chow	0.800975	True	seat_belt	0.164133	False	Pomeranian	0.017981	True
613	680145970311643136	https://pbs.twimg.com/media/CXBdJxLUsAAWql2.jpg	1	miniature_poodle	0.457117	True	toy_poodle	0.226481	True	Maltese_dog	0.067682	True
15	666099513787052032	https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg	1	Lhasa	0.582330	True	Shih-Tzu	0.166192	True	Dandie_Dinmont	0.089688	True
2000	876120275196170240	https://pbs.twimg.com/media/DCiavj_UwAAcXep.jpg	1	Bernese_mountain_dog	0.534327	True	Saint_Bernard	0.346312	True	Greater_Swiss_Mountain_dog	0.094933	True
1252	747963614829678593	https://pbs.twimg.com/media/CmFM7ngXEAEitfh.jpg	1	kelpie	0.307672	True	Irish_terrier	0.197486	True	dingo	0.105475	False

	tweet_id	img_num	p1_conf	p2_conf	p3_conf
count	2.075000e+03	2075.000000	2075.000000	2.075000e+03	2.075000e+03
mean	7.384514e+17	1.203855	0.594548	1.345886e-01	6.032417e-02
std	6.785203e+16	0.561875	0.271174	1.006657e-01	5.090593e-02
min	6.660209e+17	1.000000	0.044333	1.011300e-08	1.740170e-10
25%	6.764835e+17	1.000000	0.364412	5.388625e-02	1.622240e-02
50%	7.119988e+17	1.000000	0.588230	1.181810e-01	4.944380e-02
75%	7.932034e+17	1.000000	0.843855	1.955655e-01	9.180755e-02
max	8.924206e+17	4.000000	1.000000	4.880140e-01	2.734190e-01

	tweet_id	timestamp	text	rating	dog_stage
1575	687476254459715584	2016-01-14 03:28:06+00:00	This is Curtis. He's a fluffball. 11/10 would ...	1.1	pupper
1157	724405726123311104	2016-04-25 01:12:38+00:00	This is Ashleigh. She's having Coachella withd...	1.0	pupper
2049	671504605491109889	2015-12-01 01:42:28+00:00	This is Charlie. He was just informed that dog...	1.1	NaN
707	785515384317313025	2016-10-10 16:20:36+00:00	Today, 10/10, should be National Dog Rates Day	1.0	NaN
1571	687704180304273409	2016-01-14 18:33:48+00:00	Say hello to Blakely. He thinks that's a hat. ...	0.9	pupper

	tweet_id	retweet_count	favorite_count	jpg_url	img_num	dog_type	confidence
985	710272297844797440	1425	4945	https://pbs.twimg.com/media/Cdtk414WoAIUG0v.jpg	1	old_english_sheepdog	0.586307
677	757725642876129280	1391	5022	https://pbs.twimg.com/media/CoP7c4bWcAAr55g.jpg	2	labrador_retriever	0.128128
401	809220051211603969	6554	22246	https://pbs.twimg.com/media/CzrtWDbWEAAmIhy.jpg	1	pomeranian	0.819511
718	751251247299190784	6695	13791	https://pbs.twimg.com/ext_tw_video_thumb/75125...	1	walker_hound	0.178852
1840	668986018524233728	183	578	https://pbs.twimg.com/media/CUi3PIrWoAAPvPT.jpg	1	chihuahua	0.005640

	tweet_id	retweet_count	favorite_count
1138	727685679342333952	720	3206
331	832769181346996225	43	0
1228	713411074226274305	1440	4802
2109	670442337873600512	213	690
1184	718540630683709445	1137	2730

	tweet_id	timestamp	text	jpg_url	img_num	dog_type	dog_stage	rating	confidence	retweet_count	favorite_count
1966	666049248165822465	2015-11-16 00:24:50+00:00	Here we have a 1949 1st generation vulpix. Enj...	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	NaN	0.5	0.560311	41	111
1967	666044226329800704	2015-11-16 00:04:52+00:00	This is a purebred Piers Morgan. Loves to Netf...	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	rhodesian_ridgeback	NaN	0.6	0.408143	147	311
1968	666033412701032449	2015-11-15 23:21:54+00:00	Here is a very happy pup. Big fan of well-main...	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	german_shepherd	NaN	0.9	0.596461	47	128
1969	666029285002620928	2015-11-15 23:05:30+00:00	This is a western brown Mitsubishi terrier. Up...	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	NaN	0.7	0.506826	48	132
1970	666020888022790149	2015-11-15 22:32:08+00:00	Here we have a Japanese Irish Setter. Lost eye...	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	welsh_springer_spaniel	NaN	0.8	0.465074	532	2535